Identification of the coupling functions between the process and the degradation dynamics by means of the variational Bayesian inference: an application to the solid-oxide fuel cells

DOI10.1098/RSTA.2019.0086zbMath1462.92059OpenAlexW2982094723WikidataQ90960507 ScholiaQ90960507MaRDI QIDQ4993459

Đani Juričić, Pavle Boškoski, Boštjan Dolenc

Publication date: 15 June 2021

Published in: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences (Search for Journal in Brave)

Full work available at URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6834003

zbMATH Keywords

prediction energy algorithmic information theory computational physics degradation applied mathematics systems theory fuel cell variational Bayesian inference coupling functions power and energy systems nonlinear dynamic modelling

Mathematics Subject Classification ID

Classical flows, reactions, etc. in chemistry (92E20)

Cites Work

Monte Carlo Methods

This page was built for publication: Identification of the coupling functions between the process and the degradation dynamics by means of the variational Bayesian inference: an application to the solid-oxide fuel cells